Integrated Microbiome
Analysis Pipelines

Fostering continuous improvement and integration

Quick Glimpse

…Placeholder for cover image…

Structure of this guide

This is an example report showing the major parts of microbiome data analysis workflows.

---

Mothur Microbiome Snakemake Workflow

We use snakemake¹ to maintain reproducibility and improvement of the bioinformatics analysis of microbiome data.

Reference links

• https://github.com/wclose/mothurPipeline

Example of common FAQs (in progress)

1. Mothur dist.seqs taking too long.
• Length of the merged reads (merged reads over 300pb).
• Not enough overlaping when merging the paired reads.
• Use other pre-cluster algorithm like Deblur.
• Too many uniques?
• Not enough computer power?: Use HPC or Cluster.
• Try to make contigs using maxee instead of deltaq or use phylogeny approach for downstream analysis, or use clister.split based on taxonomy.
• do not forget to use controls.

---

Last updated on January 18, 2023.

These workflows are continuously reviewed, integrated, tested and compiled by Teresia Mrema-Buza.
Github_Repo: https://github.com/tmbuza/imap-mothur.
Github_Page (this document): https://tmbuza.github.io/imap-mothur/.
Visit the Complex Data Insights (CDI) website for more practical user guides (…in progress).

References

1. Weißbecker, C., Schnabel, B., & Heintz-Buschart, A. (2021). Dadasnake, a snakemake implementation of DADA2 to process amplicon sequencing data for microbial ecology. GigaScience, 9. https://doi.org/10.1093/gigascience/giaa135